Distantly-supervised information extraction systems extract relation tuples with a set of pre-defined relations from text. Traditionally, researchers [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]} use pipeline approaches where a named entity recognition (NER) system is used to identify the entities in a sentence and then a classifier is used to find the relation (or no relation) between them. However, due to the complete separation of entity detection and relation classification, these models miss the interaction between multiple relation tuples present in a sentence. In this approach, models find the relation only between two given entities in a sentence and do not explicitly consider the other entities and relations present in the same sentence. Thus these models miss the interaction among multiple tuples while classifying the relation between two entities.
